SOTAVerified

Content-Based Image Retrieval

Content-Based Image Retrieval is a well studied problem in computer vision, with retrieval problems generally divided into two groups: category-level retrieval and instance-level retrieval. Given a query image of the Sydney Harbour bridge, for instance, category-level retrieval aims to find any bridge in a given dataset of images, whilst instance-level retrieval must find the Sydney Harbour bridge to be considered a match.

Source: Camera Obscurer: Generative Art for Design Inspiration

Papers

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Showing 11–20 of 195 papers

TitleStatusHype
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval—0
A Decade Survey of Content Based Image Retrieval using Deep Learning—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
A Fast Content-Based Image Retrieval Method Using Deep Visual Features—0
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization—0
A Bag of Visual Words Model for Medical Image Retrieval—0
An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium—0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—0
Efficient Object Embedding for Spliced Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LHRRMAP90.94—Unverified